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This paper presents a method based on feature selection to obtain sets of Human Activity Recognizers of different complexity . Classifiers for human activity recognition are built exploring a space of candidate feature subsets, trying to maximize the accuracy of a classifier trained with them. At the same time, the size of the selected feature subset is minimized. The accuracy of a classifier tends to grow with the number of features, but in a real time task, like Human Activity Recognition, the number of features used has to be minimized, because its growing involves a slower processing rate. A set of solutions with different trade-offs between accuracy and number of features may be achieved modeling the problem of feature selection using Multiobjective Optimization (MO), where both measures are optimized at the same time. To solve the MO problem, Multiobjective Optimization Evolutionary Algorithms (MOEA) are going to be used. MOEA methods based on Pareto dominance not only find an optimal solution for the problem, they find a set of different optimal solutions so called Pareto-optimal set. A set of activity recognizers of different complexities is found using this approach. Having a set of different solutions allows the designer to choose the one that best fits its requirements. The method will be applied using a Hidden Markov Model as classifier. Results of the use of the method for recognizing different instantaneous human activities are discussed.